Evolutionary algorithms

One of the problems of the responsible of energy production and distribution is the maintaining of an appropriate voltage profile. This task can be done by the minimization of the active losses in the transportation and transmission lines by implantation of reactive power sources to the load buses. In addition to the minimization of the active losses, other criteria can be considered as the compensation devices cost and the voltage deviation. The problem to solve is multi criteria under constraints related to the voltages, the reactive productions, the compensation devices cost and the active losses. Its resolution requires the use of the advanced algorithms. In this paper, we propose an approach based on the evolutionary algorithms (AE) to solve this problem multi criterion. It is about the SPEA2 method (Improving Strength Pareto Evolutionary Algorithm).

Contact effects in organic thin film transistors (OTFTs) remain an important problem to be solved in these devices. Therefore, the correct physio-chemical modeling of the contact regions in OTFTs is necessary. In this work, a standard model for the contact region of OTFTs is proposed. It is a versatile model that describes the current-voltage characteristics of different kinds of contacts. It reproduces the behavior of Schottky barrier or space-charge limited contacts. It is a simple unified model since only a single parameter is necessary in order to distinguish between both kinds of contacts. The model is easily integrated in a generic compact model for the current-voltage characteristics of OTFTs. The resulting compact model, used in combination with an evolutionary parameter extraction procedure, allows to extract the intrinsic parameters and the current-voltage curves at the contact of single short-channel transistors. There is no need to use transistors with multiple channel lengths to accurately characterize the contact region or the active channel of the transistor. The model is tested with published experimental data of OTFTs with Schottky barrier or space-charge limited contacts. Finally, the method has been used as a diagnostic tool to analyze how an ammonia sensor reacts to different concentrations of the ammonia gas. Interestingly, alterations in the contact region have been detected when the gas concentration varies, transforming the space-charge limited contact of a pristine OTFT into a Schottky barrier contact under the exposure of gas.

Quantum vortex structures and energy cascades are examined for two dimensional quantum turbulence (2D QT) at zero temperature. A special unitary evolution algorithm, the quantum lattice algorithm (QLA), is employed to simulate the Bose-Einstein condensate (BEC) governed by the Gross-Pitaevskii (GP) equation. A parameter regime is uncovered in which, as in 3D QT, there is a short Poincaré recurrence time. It is demonstrated that such short recurrence times are destroyed by stronger nonlinear interaction. The similar loss of Poincaré recurrence is also seen in the 3D GP equation . Various initial conditions are considered in an attempt to discern if 2D QT exhibits inverse cascades are is seen in 2D CT (classical turbulence). In our simulation parameter regimes, no dual cascades spectra were observed for 2D QT-unlike that seen in 2D CT.

The MorphoEvoShorQS algorithm represents a hybrid approach to integer factorization, integrating classical methods such as Trial Division, Pollards Rho, and the Elliptic Curve Method (ECM) with a novel correlated oscillatory loop inspired by Shors quantum algorithm and the Quadratic Sieve (QS). This paper elucidates the algorithms design, theoretical foundations, and performance on large composite numbers, including a 100-digit composite number. We analyze its computational efficiency, explore its implications for cryptography, and discuss its impact on physics, computer science, and related fields. The algorithms ability to factor large numbers efficiently suggests potential vulnerabilities in cryptographic systems like RSA and provides insights into the computational complexity of quantum-inspired classical algorithms. We also examine the theoretical connections to quantum mechanics and evolutionary computing, highlighting future research directions and interdisciplinary applications.

The MorphoEvoShorQS algorithm represents a hybrid approach to integer factorization, integrating classical methods such as Trial Division, Pollard's Rho, and the Elliptic Curve Method (ECM) with a novel correlated oscillatory loop inspired by Shor's quantum algorithm and the Quadratic Sieve (QS). This paper elucidates the algorithm's design, theoretical foundations, and performance on large composite numbers, including a 100digit RSA challenge number. We analyze its computational efficiency, explore its implications for cryptography, and discuss its impact on physics, computer science, and related fields. The algorithm's ability to factor large numbers efficiently suggests potential vulnerabilities in cryptographic systems like RSA and provides insights into the computational complexity of quantum-inspired classical algorithms. We also examine the theoretical connections to quantum mechanics and evolutionary computing, highlighting future research directions and interdisciplinary applications.

In this paper, we describe the LIA system proposed for the MediaEval 2013 Crowdsourcing for Social Multimedia task. The aim is to associate an accurate label to an image among multiple noisy labels collected from a crowdsourcing platform. In particular, the task participants have to predict two types of binary labels for each considered image. The first one mentions that an image is truly fashion-related or not, while the second label indicates that the fashion tag assigned to the image is related to the content of the image or not. The proposed system combines noisy crowdsourcing labels, image metadata and external resources.

Sunto: Il testo esamina le caratteristiche della complessità sistemica dell'universo, considerando la loro funzione evolutiva. Il paradigma che sottende a tutto il discorso è la teoria cosmologica definita dalla Relatività Generale di Einstein. Inizialmente viene analizzato il dualismo dialogico entropia / sintropia, mettendo in luce il ruolo della conoscenza come protagonista del dialogo tra tendenze complementari che ha animato l'evoluzione universale dalla iniziale "singolarità" allo stato attuale. Successivamente vengono presentati gli episodi principali delle ricerche che hanno portato a definire la "scienza della complessità" e a valorizzarne gli aspetti di carattere universale, a priori di qualsiasi specificità dei diversi ambiti scientifici, sociali, ambientali. Quindi viene analizzato il ruolo dell'umanità come rappresentante primaria della complessità, e proposta una visione che suggerisce i passi per una pacificazione globale, allo scopo di liberare risorse per una presa di responsabilità nell'orientare l'attuale cambiamento climatico in direzione non distruttiva, e possibilmente vantaggiosa, per la biosfera.

Finding an efficient data reduction method for largescale problems is an imperative task. In this paper, we propose a similarity-based self-constructing fuzzy clustering algorithm to do the sampling of instances for the classification task. Instances that are similar to each other are grouped into the same cluster. When all the instances have been fed in, a number of clusters are formed automatically. Then the statistical mean for each cluster will be regarded as representing all the instances covered in the cluster. This approach has two advantages. One is that it can be faster and uses less storage memory. The other is that the number of new representative instances need not be specified in advance by the user. Experiments on real-world datasets show that our method can run faster and obtain better reduction rate than other methods.

Nowadays we find more and more applications for data mining techniques in e-learning and web-based adaptive educational systems. The useful information discovered can be used directly by the teacher or author of the course in order to improve instructional/learning performance. This can, however, imply a lot of work for the teacher who can greatly benefit from the help of educational recommender systems for doing this task. In this paper we propose a system oriented to find, share and suggest the most appropriate modifications to improve the effectiveness of the course. We describe an iterative methodology to develop and carry out the maintenance of web-based courses to which we have added a specific data mining step. We apply association rule mining to discover interesting information through students' usage data in the form of IF-THEN recommendation rules. We have also used a collaborative recommender system to share and score the recommendation rules obtained by teachers with similar profiles along with other experts in education. Finally, we have carried out experiments with several real groups of students using a web-based adaptive course. The results obtained demonstrate that the proposed architecture constitutes a good starting point to future investigations in order to generalize the results over many course contents.

This paper presents a comparative analysis of improper plumbing and ventilation installations across residential and commercial buildings in Asia, Europe, and the United States. Drawing on field observations and documented case studies, it identifies systemic failures in code enforcement, substandard construction practices, and cultural differences in building design that contribute to the spread of sewer gases and offensive odors. In countries such as China, Thailand, and Cambodia, the absence or poor implementation of plumbing codes has led to widespread sewer gas leakage due to missing water traps and exposed drain lines. In South Korea, the widespread use of wetroom bathroom designs, where sinks and showers drain directly onto the floor, reflects cultural preferences for space efficiency but raises concerns about hygiene and crosscontamination. In Russia, the installation of electric fans into natural ventilation systems during apartment renovations disrupts airflow and causes odor migration between units. European examples, such as condensation on toilet cisterns in Italy and missing trap inserts in Polish hostels, highlight additional regional challenges. In contrast, the United States benefits from strict code enforcement, licensed contractors, and centralized mechanical ventilation systems, which largely prevent such issues. However, isolated problems still occur, particularly when renovations alter fixture elevations without proper adjustments. The paper concludes that effective mitigation requires not only robust building codes but also consistent enforcement, professional training, and public awareness to ensure safe and healthy indoor environments globally.

In this study, the reliability analysis was performed for the aerodynamic analysis. Among various reliability analysis methods, the moment method was used, and to parameterize an airfoil shape the PARSEC function was applied. The accuracy of the reliability analysis was compared with other method and it was found that the moment method predicted the probability accurately.

Accurate biomechanical models of the human body were essential for understanding, predicting, and simulating human movement. While complex, multi-segment models offered high fidelity, their computational cost often limited their practical application. This study focused on optimizing a simplified three-link lower limb model to enhance its dynamic performance by incorporating impact of ground reaction forces while maintaining computational efficiency. To address the model's limitations in capturing complex human movements, evolutionary algorithms were employed to refine its parameters. The Ant Lion optimizer, Cuckoo search, Dragonfly algorithm, and Fminsearch algorithm were utilized to determine optimal values for link lengths, mass distribution, and joint stiffness. The model performance was assessed by comparing simulated lower limb with ground reaction forces, joint torques, and angles through experimental data from dynamic tasks. The fitness functions were multi-objective in nature to simultaneously minimize the three lower limb angle prediction errors. The Ant Lion optimizer demonstrated a significant advantage in terms of convergence rate and dynamic model parameter optimization with minimal root mean square error. While the Fminsearch algorithm caused overfitting of parameters, making it unsuitable for the current application, it could be useful in hybrid optimization techniques. This research aimed to identify the most suitable optimization algorithm for improving model accuracy and to explore the trade-offs between model simplicity and dynamic fidelity.

This paper introduces a hybrid optimization approach that combines genetic algorithms with gradient descent for effective nonlinear function approximation in highdimensional data. Traditional methods struggle with computational efficiency and accuracy in such complex spaces. By integrating genetic algorithms to provide a global search strategy with gradient descent for finetuning, the proposed method achieves faster convergence and improved accuracy. Simulations and case studies demonstrate its effectiveness in applications like data mining, image recognition, and financial modeling.

This paper is intended to present optimal route finding system for road network application. It is used to operate in complicated mapping situations and new unknown environments. This technique can be implemented to find the optimal path from source to destination in region of map. This system use A* and Dijkstra algorithms to draw plan and develop the Geographic Information System(GIS) assisted optimal road network route finding and give shortest path. The optimal (or) shortest path based on distance or time or cost. And then evaluates the performance of these two algorithms.

Analyzing user behavior on social networks to understand psychological traits is of growing importance. Structuring data extracted from users' concurrent activity across various platforms as a multiplex network can provide valuable insights. However, conventional approaches are limited by their reliance on explicit relationships, which are often private, and their use of single-source data. Therefore, this paper proposes a novel two-stage framework for personality type prediction. Its primary innovation is the use of an implicit relationship network, extracted from the fusion of multi-source, multi-modal data including text, image, and location, instead of relying on explicit friendship links. In the first stage, community structures in the multiplex network are identified using a multi-objective evolutionary approach (MOEA/D-TS), and then the implicit relationship network is constructed using a link prediction method (CLPES). In the second stage, the resulting network is leveraged as an information source within a machine learning framework to predict user personality types. Evaluations demonstrate the proposed framework's significant superiority. The final model, using a Random Forest classifier with the fusion of all three data sources, achieved an average Macro-F1 score of 0.673. This represents an average performance improvement of 23% compared to single-source models. This finding confirms the value of analyzing implicit relations and fusing multi-source data for a deeper understanding of user personality.

Evolutionary testing is a very popular domain in the field of search based software e n g i n e e r i n g that consists i n automatically generating test data for a given piece of code using evolutionary algorithms. One of the most important measures used to evaluate the quality of the generated test suites is code coverage. In this paper we first analyze if there exists a correlation between some static measures computed on the test program and the code coverage when an evolutionary test data generator is used. In particular, we use and compare three techniques for the search engine of the test data generator: an Evolutionary Strategy, a Genetic Algorithm, and a Random Search. We have also developed a program generator that is able to create Java programs with the desired values for the given static measures. Our experimental study includes a benchmark of 1800 programs automatically generated. In addition to the correlations study we also analyze the subset of programs for which one algorithm is better than another one. This second analysis could be the basis for the development of a software tool that automatically decides the suitable test data generation search engine according to the static measures computed on the test object.

Resumen. Evolutionary testing is a very popular domain in the field of search based software engineering that consists in automatically generating test cases for a given piece of code using evolutionary algorithms. One of the most important measures used to evaluate the quality of the generated test suites is code coverage. In this paper we want to analyze if there exists a correlation between some static measures computed on the test program and the code coverage when an evolutionary test case generator is used. In particular, we use Evolutionary Strategies (ES) as search engine of the test case generator. We have also developed a program generator that is able to create Java programs with the desired values of the static measures. The experimental study includes a benchmark of 3600 programs automatically generated to find correlations between the measures. The results of this study can be used in future work for the development of a tool that decides the test case generation method according to the static measures computed on a given program.

Combinatorial Interaction Testing (CIT) is a technique used to discover faults caused by parameter interactions in highly configurable systems. These systems tend to be large and exhaustive testing is generally impractical. Indeed, when the resources are limited, prioritization of test cases is a must. Important test cases are assigned a high priority and should be executed earlier. On the one hand, the prioritization of test cases may reveal faults in early stages of the testing phase. But, on the other hand the generation of minimal test suites that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search based approaches are required to find the (near) optimal test suites. In this work we present a novel evolutionary algorithm to deal with this problem. The experimental analysis compares five techniques on a set of benchmarks. It reveals that the evolutionary approach is clearly the best in our comparison. The presented algorithm can be integrated into CTE XL professional tool.

Context: Complexity measures provide us some information about software artifacts. A measure of the difficulty of testing a piece of code could be very useful to take control about the test phase. Objective: The aim in this paper is the definition of a new measure of the difficulty for a computer to generate test cases, we call it Branch Coverage Expectation (BCE). We also analyze the most common complexity measures and the most important features of a program. With this analysis we are trying to discover whether there exists a relationship between them and the code coverage of an automatically generated test suite. Method: The definition of this measure is based on a Markov model of the program. This model is used not only to compute the BCE, but also to provide an estimation of the number of test cases needed to reach a given coverage level in the program. In order to check our proposal, we perform a theoretical validation and we carry out an empirical validation study using 2600 test programs. Results: The results show that the previously existing measures are not so useful to estimate the difficulty of testing a program, because they are not highly correlated with the code coverage. Our proposed measure is much more correlated with the code coverage than the existing complexity measures. The high correlation of our measure with the code coverage suggests that the BCE measure is a very promising way of measuring the difficulty to automatically test a program. Our proposed measure is useful for predicting the behaviour of an automatic test case generator.

Selection of membership functions (MF) for a fuzzy logic controller (FLC) is an iterative and a time consuming task. This paper describes the use of genetic algorithm (GA) search strategy for optimizing MFs of antecedent and consequent variables of an FLC that has been used for controlling the level of water on real time basis in a laboratory scale pilot plant. For the simulation of GA integral time square error (ITSE), has been used as the fitness function.

The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones. In this paper we focus on the subproblem of global optimization for differentiable functions and we propose an Evolutionary Search-inspired solution where we model point search distributions via Generative Neural Networks. This approach enables us to model diverse and complex search distributions based on which we can efficiently explore complicated objective landscapes. In our experiments we show the practical superiority of our algorithm versus classical Evolutionary Search and gradient-based solutions on a benchmark set of multimodal functions, and demonstrate how it can be used to accelerate Bayesian Optimization with Gaussian Processes.

Situated and embodied reactive agents solve relatively complex tasks by coordinating action and perception. Reactive agents are generally believed to be incapable of dealing with identical perceptual states that require different responses (i.e., perceptual ambiguity). In contrast to reactive agents, non-reactive agents can deal with perceptual ambiguity by storing and integrating sensory information over time. This paper investigates how and to what extend reactive and non-reactive agents cope with perceptual ambiguity. An active categorical perception model is introduced in which agents with different neuro-controllers categorise objects by coordinating action and perception. The agent’s neuro-controllers are evolutionary optimised for the task. Our results show that reactive agents can cope with perceptual ambiguity despite their incapability to store perceptual information over time. An analysis of behaviour reveals that while non-reactive agents use internal memory to represent...

The 'simulation hypothesis' is an intriguing explanation for cognition, and holds that 'thinking consists of simulated interaction with the environment' ([4], p.242). However, the neuroscientific proof for a simulation mechanism in the brain is indirect. In this paper we present a minimal-model approach to investigate the 'simulation hypothesis'. Our minimal model is called ACP and is an extension of the Active Categorical Perception model (ACP) presented in . In ACP , robots have a neurocontroller with an output-input feedback mechanism that allows them to simulate perception and behaviour internally. Our experiments focus on the performance of robots with three different types of neurocontroller (two feedforward and one recurrent type of neurocontroller). Their performance is compared over three experimental conditions in which the output-input feedback mechanism is functional for variable durations. The results show that feedforward-neurocontrolled robots benefit from output-input feedback, while recurrent-neurocontrolled robots do not. Based on these results, two closely related conclusions are drawn: (1) the 'simulation hypothesis' may be too specific, and (2) predicting future perception may depend on neural recurrency (i.e., internal feedback) in general, rather than on the ability to simulate perception by feeding back actions.

 Acquisition traits are those that depend, for their evolutionary (fitness) value, on the acquisition of something that gradually accumulates during an organism’s life but is not transmitted genetically to descendants. This situation causes these traits, including language, immunity, intelligence, and social status, to represent a special need for evolvability and programmed aging. Programmed aging refers to the idea that mammals and most multiparous sexually reproducing organisms evolved complex biological mechanisms that cause gradually increasing fitness deterioration and internally limit individual organism lifetimes. The rationale is that this process enhances the survival (non-extinction) of a species population. Evolvability theories propose that animals have evolved design characteristics that aid their ability to evolve, reducing the time required for a particular increment of evolutionary adaptation and/or increasing the precision with which the adaptation can be performed. Because an increase in evolvability aids a population in escaping extinction, evolution selects the associated design characteristics. Both concepts conflict with traditional (Darwinian) theory regarding details of the nature of evolution. However, more recent genetics discoveries have provided rich detail regarding the nature of the evolution process, including the fact that biological inheritance involves the transmission of organism design information from parent to descendant in digital form. These discoveries have acted to support both programmed aging and evolvability theories.

There is growing evidence to suggest that the success of a co-evolutionary learning system may depend critically on the nature of the environment in which the learner is placed, and on certain attributes of the task domain, rather than the details of the particular learning algorithm employed. We discuss how a learning system can be modeled as a meta-level game between abstract entities which w e call performer, inltrator and evaluator. Learning can sometimes fail due to collusive suboptimal equilibria in this meta-game of learning. But some domains have special attributes which seem to prevent such collusions and thereby facilitate co-evolutionary advancement. A better understanding of these issues may help to improve the design of co-evolutionary learning systems in the future.

One of the most important tasks in approximately solving an optimisation problem is to adjust the parameters of the metaheuristic used as a solution method. As the metaheuristics are usually general in purpose, it is necessary to make adjustments to them for each optimisation problem to which they are applied to get high-quality solutions. In this paper, we propose the use of a Type 1 Fuzzy Inference System and a Type 2 Fuzzy Logic Inference System to select the operators of a Genetic Algorithm during execution time to solve a set of ten test functions from the literature. The results of computational experiments show that the fuzzy selection of operators improves the performance of the original GA on nine of the ten test functions with practically the same execution time.

This work proposes a multiobjective approach for transmission expansion planning considering security constraints (N-1 criterion) and minimum cost. Elitist NSGA-II multiobjective algorithm is used and the operative problem is solved trhough high order interior point method. The methodology is tested on 24 and 6 bus IEEE systems.

Evaluating AI is a challenging task, as it requires an operative definition of intelligence and the metrics to quantify it, including amongst other factors economic drivers, depending on specific domains. From the viewpoint of AI basic research, the ability to play a game against a human has historically been adopted as a criterion of evaluation, as competition can be characterized by an algorithmic approach. Starting from the end of the 1990s, the deployment of sophisticated hardware identified a significant improvement in the ability of a machine to play and win popular games. In spite of the spectacular victory of IBM’s Deep Blue over Garry Kasparov, many objections still remain. This is due to the fact that it is not clear how this result can be applied to solve real-world problems or simulate human abilities, e.g., common sense, and also exhibit a form of generalized AI. An evaluation based uniquely on the capacity of playing games, even when enriched by the capability of learn...

In this paper, we used genetic algorithms to automate the optimal choice of 6-to 9-yearold girls for school access and retention. This method can be a complement to the classic manual strategy of going door to door or using administrative summons and the services of a town crier to achieve the same objectives.

When it comes to home maintenance, most homeowners pay close attention to visible plumbing fixtures like sinks, tubs, and toilets. Yet there's one component that frequently escapes notice until it becomes a significant issue: the floor drain. Found in basements, laundry rooms, garages, and utility areas, this drain quietly plays an important role in protecting your home from water damage. Overlooking its condition or function can lead to some unpleasant surprises.

In this paper, a 10-bit 50-Msample/s pipelined ADC by using dynamic charge injection technique is presented. By the proposed scheme, the input voltage range is increased and power consumption is reduced. For the calibration of the output codes, a new method is presented which uses polynomial inverse function. By the use of the inverse function and simultaneously adjustment of both the weights of stages and coefficients of polynomials, linearity is achieved. The proposed ADC is designed and simulated in a 90-nm CMOS technology. Simulation results show that the ADC achieves a peak signal-to-noise-and-distortion ratio (SNDR) of 59 dB, a peak spurious-free dynamic range (SFDR) of 72.5 dB. The ADC's power consumption (without calibration circuitry) is 1 mW (without calibration circuitry)

Despite the fact that pipelines offer a safe, economical way to transport natural gas and dangerous liquids, pipeline leaks and ruptures can result in disastrous consequences. Pipelines can be susceptible to different metallurgical failure mechanisms including but not limited to manufacturing defects, third party damage, and corrosion. Understanding these failure mechanisms is critical to mitigating risk of future incidents and managing the future integrity of the pipeline. The world is experiencing a significant change in the pipeline business [1-3], debating the need to continue to transport our oil and gas through ageing pipelines while facing, at the same time, to a much stringent regulatory standard. Pipelines have, and will, fail, for one reason or the other. Recent failures in the USA [4], high pressure natural gas transmission pipeline in Case Studies in Engineering Failure Analysis 7 (2016)

As the information proliferates, searching for relevant information has become a primary task. Searching or Information retrieval (IR) aims to help the users in organising as well as retrieving those documents from the documentary collection which are most likely to satisfy information needs of the user. An optimal Information Retrieval System (IRS) is one which retrieves only those documents from the document database which are pertinent to user's information needs, while excluding documents that are not relevant. Genetic Algorithm is described by higher likelihood of finding good solutions to large and complex problems of IR optimisation. The performance of Genetic Algorithm depends upon the decision of underlying operators used namely selection, crossover and mutation. A GA-based algorithm IRIGA (Information Retrieval Improvement using Genetic Algorithm) is developed to improve the performance of Information Retrieval System. This paper presents a comparison of performance of IRIGA when different selection methods are used. The results are analysed by conducting experiments keeping the rest of the GA parameters as constant and varying only the selection strategy.

Gode and Sunder's (1993) results from using "zero-intelligence" (zi) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous double-auction markets is determined more by market structure than by the intelligence of the traders in that market. However, it is demonstrated here that the average transaction prices of zi traders can vary significantly from the theoretical equilibrium value when the market supply and demand are asymmetric, and that the degree of difference from equilibrium is predictable from a priori probabilistic analysis. In this sense, it is shown here that Gode and Sunder's results are artefacts of their experimental regime. Following this, `zero-intelligence-plus' (zip) traders are introduced: like zi traders, these simple agents make stochastic bids. Unlike zi traders, they employ an elementary form of machine learning. Groups of zip traders interacting in exper...

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and co...

Does natural selection favor veridical percepts-those that accurately (if not exhaustively) depict objective reality? Perceptual and cognitive scientists standardly claim that it does. Here we formalize this claim using the tools of evolutionary game theory and Bayesian decision theory. We state and prove the "Fitness-Beats-Truth (FBT) Theorem" which shows that the claim is false: If one starts with the assumption that perception involves inference to states of the objective world, then the FBT Theorem shows that a strategy that simply seeks to maximize expected-fitness payoff, with no attempt to estimate the "true" world state, does consistently better. More precisely, the FBT Theorem provides a quantitative measure of the extent to which the fitness-only strategy dominates the truth strategy, and of how this dominance increases with the size of the perceptual space. The FBT Theorem supports the Interface Theory of Perception (e.g. , which proposes that our perceptual systems have evolved to provide a species-specific interface to guide adaptive behavior, and not to provide a veridical representation of objective reality.

Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignmen...

In this paper, the metaheuristic method consisting of the adaptive tabu search (ATS) and the practicing heuristic (PH) technique is proposed to provide optimal solutions of the assembly line balancing (ALB) problems. With the multi-objective approach, the ATS is used to address the number of tasks assigned for each workstation, while the PH is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The proposed approach is tested against six benchmark ALB problems suggested by Scholl and one real-world ALB problem. Comparisons of optimal results obtained by the proposed method with those obtained by the single objective approach are elaborated.

This paper aims to apply a modified current search method, adaptive current search (ACS), for assembly line balancing problems. The ACS algorithm possesses the memo ry list (M L) to escape fro m local entrapment and the adaptive radius (AR) mechanis m to speed up the search process. The ACS is tested against five benchmark unconstrained and three constrained optimization problems co mpared with genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms and provides superior results. The ACS is used to address the number of tasks assigned for each workstation, while the heuristic sequencing (HS) technique is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The workload variance and the idle t ime are performed as the mult iple-object ive functions. The proposed approach is tested against four benchmark A LB problems co mpared with the GA, TS and CS. As results, the ACS associated with the HS technique is capable of producing solutions superior to other techniques. In addition, the ACS is an alternative potential algorithm to solve other optimization problems.

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize differ...

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization.

In this study, we construct a complex network from the inner dynamic of Particle Swarm Optimization algorithm. The subsequent analysis of the network promises to provide useful information for better understanding the dynamic of the swarm that is not acquirable by other means. We present several network visualizations and numerical analysis. We discuss the observations and propose further directions for the research.

This research deals with a synthesis of control law by means of analytic programming for selected discrete chaotic system -Hénon Map. The novelty of the approach is that analytic programming as a tool for symbolic regression is used for the purpose of stabilization of higher periodic orbits, which represent the oscillations between several values of chaotic system. The paper consists of the descriptions of analytic programming as well as used chaotic system and detailed proposal of cost function used in optimization process. For experimentation, Self-Organizing Migrating Algorithm (SOMA) with analytic programming and Differential evolution (DE) as second algorithm for meta-evolution were used.

In this paper, evolutionary algorithm Differential Evolution (DE) is compared with swarm based technique PSO in the task of optimal evolutionary tuning of controller parameters for the stabilization of selected discrete chaotic system, which is the two-dimensional Lozi map. The novelty of the approach is that the most utilized examples of evolutionary/swarm based algorithms are compared directly on the highly nonlinear and complex multimodal optimization and simulation task. The simulations were performed for three different required final behavior of the chaotic system.

In this paper the performance of Multi-chaotic PSO algorithm is investigated using two shifted benchmark functions. The purpose of shifted benchmark functions is to simulate the time-variant real-world problems. The results of chaotic PSO are compared with canonical version of the algorithm. It is concluded that using the multi-chaotic approach can lead to better results in optimization of shifted functions.

This research deals with the synthesis of control law by means of Analytic Programming (AP) for the Hénon Map, which is discrete chaotic system. The tool for symbolic regression (AP) is used for the purpose of stabilization of stable state and higher periodic orbits, which represent oscillations between several values of chaotic system. For experimentation, Self-Organizing Migrating Algorithm (SOMA) with AP and Differential Evolution (DE) as the second algorithm for meta-evolution were used.